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281 technology companies 80,628 people laid off,why are you Data Science Careers?
You might think that now is not a good time as companies are downsizing, and yes there are layoffs, but if you look at the chart below you can see that the recent layoffs are paltry compared to what will happen at the end of 2022 and the beginning of 2023. So it’s not so bad.
sauce: Layoff.fyi
Another way to look at it is even more positive: companies are still hiring data scientists. In fact, in the last month alone, 5,500 job ads on Glassdoor US only.
The job market for data scientists is booming. Companies are now more demanding. They want data science specialists rather than generalists. Moreover, data scientists are expected to use AI tools. Here’s how you can rise to the challenge and stay on top of the job market.
1. The path of education
There are always two different approaches to learning data science.
- Academic Education
- self study
Ideally, you’ll combine both.
Academic Education
Although an academic education isn’t required to become a data scientist, it does provide a broad and codified body of knowledge, and it’s much easier to build on this knowledge later than trying to become a data scientist from scratch.
Data scientists typically hold a bachelor’s degree in a quantitative field such as computer science, statistics, mathematics, or even economics.
Earning a master’s degree can go a long way in improving your employability, as it allows you to narrow down your field of expertise. Examples of specializations include machine learning, data analytics, and business intelligence.
Unless you are interested in pursuing a research-oriented role in a company or academia, a PhD is usually not necessary.
self study
You can become a data scientist by creating a curriculum for yourself, which may include any of the following list (not exhaustive):
- Certification
- Online Courses
- Bootcamp
- YouTube videos
- Books
- Blog Post
- Community forum
If you have the time and funds, we recommend focusing on certifications, online courses, and bootcamps, then supplementing with other resources.
Some of the certifications, courses, and bootcamps I recommend are:
2. Skills
Data scientist skills can be categorized into technical skills and soft skills.
Technical skills
These stem from the primary tasks of a data scientist: extracting and manipulating data, building, testing, and deploying ML models.
Data scientists need to use a variety of programming languages and tools to put all this knowledge into practice.
The overview is as follows:
This should be a starting point for further specialization – for example, you could specialize in BI tools or focus on data engineering tools such as: Apache Kafka, Apache Spark, Talend, air currentSuch
Soft Skills
Technical skills should be complemented by the following soft skills:
Communication skills
This includes both listening to others’ ideas and sharing your own ideas.
As a data scientist, your work starts with listening to other people’s problems. You’re like a psychotherapist who helps others solve their problems using data. Are you a data therapist? Understanding the business problem helps you shape technical solutions to meet the needs of your users.
Data scientists must be able to explain the technical complexities of their work to a non-technical audience. Data scientists use visualization tools to aid in their work, so being able to effectively visualize and present your work is essential.
Analytical Thinking
The business problem that needs to be solved is often described in a very non-technical way: “Oh, our customer retention is low. Please help us. You, the data science expert, come up with something.”
This requires the ability to break problems down into logical blocks and solve them systematically, and also creativity, as many problems require finding novel solutions.
Collaboration skills
A data scientist’s ideal work day involves being alone, working on a model and talking gently to it (in Gollum’s voice). It’s mine, I tell you. It’s mine. My dear. Yes, my dear.
Unfortunately, data scientists often have to collaborate with other colleagues on the data team, and projects also include cross-functional teams.
Being adaptable and flexible, creating a positive work environment, and resolving conflicts effectively and respectfully? Yes, my loved ones!
project management
Working on a data science project requires project management skills like prioritizing tasks, coordinating a project team, and tracking project progress and deadlines.
Add to this the need to mentor junior staff and coordinate multiple projects and this skill becomes crucial.
Business Acumen
Every data project is designed to solve a business problem, and to do that you need a solid understanding of your company’s business and the industry you’re in. This will help you understand the business problem and design a solution that takes into account dependencies that aren’t explicitly mentioned.
3. Career Path and Salary
Data science careers typically include: Junior Data Analyst or Junior Data Scientist work.
From there, I would recommend moving on to the next one Role of SpecialtyExamples include Data Engineer, ML Engineer, Business Analyst, Data Analyst, BI Engineer, etc. Data Scientist roles today are also becoming more specialized roles, with more of a focus on using statistics in data exploration and initial model development, rather than working on end-to-end projects.
Depending on your years in a particular profession and your interest, you can go in two directions: Managerial or highly professional position.
for example, Management In any of the above specializations, you can become a senior manager or director. This path takes you away from the technical aspects of your job and puts you in charge of people and department management.
The other option is to stay on as an individual contributor and further develop your area of expertise. Highly Specialized RolesIn any of the above specialties, the titles are usually staff, principal, distinguished, or fellow.
4. Salary
Data science remains a very high-paying profession, something you shouldn’t overlook when choosing your career path. Here is an overview of salaries for the aforementioned roles.
Image courtesy of the author, salary data source: Glass Door
5. Employment
Now, the question is, how do you transition from learning data science to making all this money, i.e. getting a job?
If I said, “Find a job ad you like, apply, do your best in the interview, and get the job,” I wouldn’t have anything new to say. You’re welcome!
However, there are two things that can set you apart from other applicants.
- Excellent portfolio
- Job interview experience
Excellent portfolio It means having enough data projects Related To get a job, data projects are a great way to build and showcase your overall data science skills, as they require advanced levels of each skill. Of course, you can also work on specialized projects that focus on specific skills, such as machine learning, data engineering, etc.
Job interview experience You can earn them in two ways. The first is Failing many interviews Before you get a job, you have to go through an interview. This is a legitimate thing that many of us have been through. It’s no joke, and the experience helps you get used to the interview process, the approach, the topics you’ll be tested on, and especially to coding under time pressure.
But there are easier ways to achieve the same thing. Real-world coding and other technical interview questions On the platform that offers them.
Conclusion
It may not seem like it, but now is an ideal time to get started in data science, for two reasons. First, if you’re thinking about starting a data science education, by all means, get started. It will take some time, and by the time you’re done, data science may be booming again.
Secondly, if you already meet all the requirements, apply as there are plenty of jobs out there despite the layoffs.
Remember, despite all the changes, data science remains one of the most exciting jobs.
Nate Rossidy Nate is a Data Scientist, Product Strategy, Adjunct Professor of Analytics, and Founder of StrataScratch, a platform that helps data scientists prepare for interviews with real interview questions from leading companies. Nate writes about the latest trends in the career market, offers interview advice, shares data science projects, and covers all things SQL.
